Dataset Information

Transmission classification model to determine place and time of infection of tuberculosis cases in an urban area.

ABSTRACT: We conducted a population-based study in the Rotterdam region of The Netherlands to determine the place and time of infection of tuberculosis (TB) cases using conventional epidemiological and genotyping information. In particular, we focused on the extent of misclassification if genotyping was not combined with epidemiological information. Cases were divided into those with a unique mycobacterial DNA fingerprint, a clustering fingerprint, and an unknown fingerprint. We developed transmission classification trees for each category to determine whether patients were infected in a foreign country or recently (2 years) infected in The Netherlands. Of all TB cases during the 12-year study period, 38% were infected in a foreign country, 36% resulted from recent transmission in The Netherlands, and 18% resulted from remote infection in The Netherlands, while in the remaining cases (9%) either the time or place of infection could not be determined. The conventional epidemiological data suggested that at least 29% of clustered cases were not part of recent chains of transmission. Cases with unknown fingerprints, almost all culture negative, relatively frequently had confirmed epidemiological links with a recent pulmonary TB case in The Netherlands and were more often identified by contact tracing. Our findings highlight the idea that genotyping should be combined with conventional epidemiological investigation to establish the place and time of infection of TB cases as accurately as possible. A standardized way of classifying TB into recently, remotely, and foreign-acquired disease provides indicators for surveillance and TB control program performance that can be used to decide on interventions and allocation of resources.

SUBMITTER: de Vries G

PROVIDER: S-EPMC2593293 | biostudies-literature | 2008 Dec

REPOSITORIES: biostudies-literature

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Transmission classification model to determine place and time of infection of tuberculosis cases in an urban area.

de Vries G G Baars H W M HW Sebek M M G G MM van Hest N A H NA Richardus J H JH

Journal of clinical microbiology 20081008 12

We conducted a population-based study in the Rotterdam region of The Netherlands to determine the place and time of infection of tuberculosis (TB) cases using conventional epidemiological and genotyping information. In particular, we focused on the extent of misclassification if genotyping was not combined with epidemiological information. Cases were divided into those with a unique mycobacterial DNA fingerprint, a clustering fingerprint, and an unknown fingerprint. We developed transmission cla ...[more]

PMID: 18842933

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Project description:Leptospirosis, a spirochaetal zoonotic disease, is the cause of epidemics associated with high mortality in urban slum communities. Infection with pathogenic Leptospira occurs during environmental exposures and is traditionally associated with occupational risk activities. However, slum inhabitants reside in close proximity to environmental sources of contamination, suggesting that transmission during urban epidemics occurs in the household environment.A survey was performed to determine whether Leptospira infection clustered within households located in slum communities in the city of Salvador, Brazil. Hospital-based surveillance identified 89 confirmed cases of leptospirosis during an outbreak. Serum samples were obtained from members of 22 households with index cases of leptospirosis and 52 control households located in the same slum communities. The presence of anti-Leptospira agglutinating antibodies was used as a marker for previous infection. In households with index cases, 22 (30%) of 74 members had anti-Leptospira antibodies, whereas 16 (8%) of 195 members from control households had anti-Leptospira antibodies. Highest titres were directed against L. interrogans serovars of the Icterohaemorrhagiae serogroup in 95% and 100% of the subjects with agglutinating antibodies from case and control households, respectively. Residence in a household with an index case of leptospirosis was associated with increased risk (OR 5.29, 95% CI 2.13-13.12) of having had a Leptospira infection. Increased infection risk was found for all age groups who resided in a household with an index case, including children <15 years of age (P = 0.008).This study identified significant household clustering of Leptospira infection in slum communities where recurrent epidemics of leptospirosis occur. The findings support the hypothesis that the household environment is an important transmission determinant in the urban slum setting. Prevention therefore needs to target sources of contamination and risk activities which occur in the places where slum inhabitants reside.

Project description:BackgroundDengue, a vector-borne infectious disease caused by the dengue virus, has spread through tropical and subtropical regions of the world. All four serotypes of dengue viruses are endemic in the equatorial city state of Singapore, and frequent localised outbreaks occur, sometimes leading to national epidemics. Vector control remains the primary and most effective measure for dengue control and prevention. The objective of this study is to develop a novel framework for producing a spatio-temporal dengue forecast at a neighbourhood level spatial resolution that can be routinely used by Singapore's government agencies for planning of vector control for best efficiency.MethodsThe forecasting algorithm uses a mixture of purely spatial, purely temporal and spatio-temporal data to derive dynamic risk maps for dengue transmission. LASSO-based regression was used for the prediction models and separate sub-models were constructed for each forecast window. Data were divided into training and testing sets for out-of-sample validation. Neighbourhoods were categorised as high or low risk based on the forecast number of cases within the cell. The predictive accuracy of the categorisation was measured.ResultsClose concordance between the projections and the eventual incidence of dengue were observed. The average Matthew's correlation coefficient for a classification of the upper risk decile (operational capacity) is similar to the predictive performance at the optimal 30% cut-off. The quality of the spatial predictive algorithm as a classifier shows areas under the curve at all forecast windows being above 0.75 and above 0.80 within the next month.ConclusionsSpatially resolved forecasts of geographically structured diseases like dengue can be obtained at a neighbourhood level in highly urban environments at a precision that is suitable for guiding control efforts. The same method can be adapted to other urban and even rural areas, with appropriate adjustment to the grid size and shape.

Project description:BackgroundThe risk of infection from respiratory pathogens increases according to the contact rate between the infectious case and susceptible contact, but the definition of adequate contact for transmission is not standard. In this study we aimed to identify factors that can explain the level of contact between tuberculosis cases and their social networks in an African urban environment.MethodsThis was a cross-sectional study conducted in Kampala, Uganda from 2013 to 2017. We carried out an exploratory factor analysis (EFA) in social network data from tuberculosis cases and their contacts. We evaluated the factorability of the data to EFA using the Kaiser-Meyer-Olkin Measure of Sampling Adequacy (KMO). We used principal axis factoring with oblique rotation to extract and rotate the factors, then we calculated factor scores for each using the weighted sum scores method. We assessed construct validity of the factors by associating the factors with other variables related to social mixing.ResultsTuberculosis cases (N?=?120) listed their encounters with 1154 members of their social networks. Two factors were identified, the first named "Setting" captured 61% of the variance whereas the second, named 'Relationship' captured 21%. Median scores for the setting and relationship factors were 10.2 (IQR 7.0, 13.6) and 7.7 (IQR 6.4, 10.1) respectively. Setting and Relationship scores varied according to the age, gender, and nature of the relationship among tuberculosis cases and their contacts. Family members had a higher median setting score (13.8, IQR 11.6, 15.7) than non-family members (7.2, IQR 6.2, 9.4). The median relationship score in family members (9.9, IQR 7.6, 11.5) was also higher than in non-family members (6.9, IQR 5.6, 8.1). For both factors, household contacts had higher scores than extra-household contacts (p?<?.0001). Contacts of male cases had a lower setting score as opposed to contacts of female cases. In contrast, contacts of male and female cases had similar relationship scores.ConclusionsIn this large cross-sectional study from an urban African setting, we identified two factors that can assess adequate contact between tuberculosis cases and their social network members. These findings also confirm the complexity and heterogeneity of social mixing.

Dataset Information

Transmission classification model to determine place and time of infection of tuberculosis cases in an urban area.

Publications

Transmission classification model to determine place and time of infection of tuberculosis cases in an urban area.

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